Happy Thanksgiving!

November 26th, 2015, 3:30pm by Sam Wang

I know it’s a time for people to come together, eat, and get into conversation. Sometimes things can get contentious. Here’s a primer on What To Say To Your Frequentist Uncle. Lots of good stuff there, including a question on what “never” means. Happy Thanksgiving, everyone!

Update: This was a joke post. The rickrolling concept is explained here.

Commenter Evan wrote “no, seriously, explain this Bayesian thing to me!” To learn what the fuss is about, read this essay from Better Explained. Basically, the simplest probability calculations often start from assumptions like “what if your hypothesis is true?” or “what if your hypothesis is false?”, whereas real-life probabilities do not meet these criteria. This essay includes the classic example of breast cancer testing, a useful point of view for most people.

If you are a statistics student, maybe think of the difference between frequentists and Bayesians. Frequentists start with the idea that an event is one of many examples that obey some hypothesis (e.g. “the coin is fair”) and calculate the frequency with which the event might occur. Bayesians start with observations and use them to update their opinion about the likelihood of various hypotheses. Here are takes from For Dummies and QuantStart.

If you are a working scientist: here is an excellent essay by David Colquhoun on false discovery and the misinterpretation of p-values. In particular, Figure 2 contains a great lesson in how to think about p-values. Attention-grabbing quote: “If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time.” Yikes! Basically, the p-value is a frequentist calculation, which for scientific use works best if it is placed in a broader framework. You can call the broader framework “Bayesian” and feel very au courant.

If you have a favorite book or article to recommend, please say so in comments…

OK, Sam, I’ve been pranked. I admit to being a gullible person who tends to trust people until they prove themselves untrustworthy.

So, does this one experiment prove you untrustworthy, after all your great election analyses in the past? Is that what I should ask my Uncle? And, should I believe anything he says, him being a frequentist and all?

Should I believe the next thing you say? What if you were the only person who could help me answer that?

Happy Thanksgiving all. For advanced data analysis tutorials you can’t beat Mr. Nate Silver’s most recent “don’t freak out about Trump” message which redefines 30% as the new 6% and it’s waaay too early so stop doing the math already.

I have noticed that Silver is sticking to his story about it being too early. He might be right, but the problem is that we are on some fairly strange territory, which leads to unpredictability.

If one looks at GOP state-by-state rules, 30% is actually the new 55%. As I was writing elsewhere, 20% support is actually enough to get a first-ballot majority of delegates, as long as nobody else reaches 20%. So that’s Rubio’s target. However, that requires support to be perfectly uniform across the 57 state/territorial races. A little more calculation is needed to take into account the variation. I’ve been holding back because it seems a bit early to be saying such things.

The problem with Silver and all these other experts is that their analysis is hardly independent of candidate rankings. If Bush or Christie were polling at 30+% and Trump et al were at 6% I doubt the “don’t believe poll numbers” chorus would be humming their tune.

This is why blind analyses were invented. We all have biases. Bayes just forces you to admit up front what they are.

I was talking with a law prof. at U. Chicago and realized that the “intents and effects” standard basically contain Bayesian reasoning within it. “Intents” establishes the prior that something could have plausibly happened. Then, a simpler test provides a simple frequentist test. Together that’s a Bayesian approach, even though it seems not to be called that.

Did you see Nate’s response (“PhDemocrat”) to you in Tuesday’s column? Are you going to write a rebuttal to him? In fairness to him, he makes some compelling points–the demographics of Iowa in particular distort the actual support someone like Trump could count on nationally, and he again makes a relatively credible argument that, in fact, most voters have not made a decision about who they’ll support in their primary…